R - Retention

About Retention

The retention score is a measure of how many of your users are coming back to your application.

Why use it

Users will continue using an application and repeatedly return to it if they find value in it.

A low retention indicates that users might not see value in interacting regularly with an application or struggle to achieve the intended usage. On the other hand, a high Retention Score significantly impacts your business results, leading to a higher return on investment (ROI) for your app.

Retention analytics metrics overview page

How is it calculated?

R score
Average of the Retention Rate and Stickiness
Returning usersThe metric compares the number of users who returned to the application during the selected period with the users from the previous period.
Retention rateRatio of returning users to all users (comparing with the previous period).
StickinessAverage daily stickiness in the selected date range in the date picker. Daily stickiness is the ratio of Weekly Active Users to Monthly Active Users on that day (WAU/MAU)

Funnel "How often your users return?" 

Based on you selected time range in the date picker, we take all returning users from this period and look at the behaviour of these returning users from the past to classify them into monthly, weekly or daily group. For this we look at the last 90 days (or since user's first session) and calculate number of sessions, days, weeks and months they returned.

Monthly Active UserThe user was active 2 out of 3 months
Weekly Active UserUser active 75% of weeks in this period (about 9 out of 12)
Daily Active UserA weekly user that’s also active >40% of the days in this period (about 3/7)
Multiple times per dayA daily user that has more engaged sessions than days

The warmup period

Keep in mind, some retention metrics require warmup period: 

  • Stickiness compares weekly to monthly active users. In the first 2-3 weeks HEART does not know the number of your monthly users, so stickiness will be higher than usual.
  • Your Retention rate compares users with the previous period, so if there is no data in this period, retention will appear higher in the beginning. 


HEART may encounter difficulties in accurately assessing returning users in these scenarios: 

  • When users switch devices: HEART relies on the browser to determine if a user is returning, either using cookies or through the Browser Extension
  • Cookie-related problems:  For instance, if a user's browser blocks or restricts cookies, it can hinder the ability of HEART to identify them as returning users accurately. Using the SSO to identify the user is a good way to mitigate the issue. 

Best Practices

To get the most value in understanding and improving the Retention numbers, we suggest monitoring the numbers for at least 1 month. 

Here are our Tips for Retention: 

1. Define user groups

There might be different expectations for different users on how often they should be returning to which application or page. Define those user groups and create user segments, so you can better analyze the data.

2. Understand why 

Conduct user research to understand why users are not retaining. How much value do people get from the application? What is the application being used for? 

Understand whether there might be roadblocks in using the application. Something might be broken that keeps people from coming back / creates frustration.

3. Improve onboarding

Send users an email to invite them into your application and highlight the values of it. 

4. Awareness and promotion

Send a reminder over email or other internal channels.

5. Check for limitations 

Check if your system is restricting cookies from the user's browser.

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